Exploring Multi-Static Numerical Techniques for IPM Motors: AI-Powered Review

Abstract

The technical aspects of the multi-static computation of interior permanent magnet (IPM) motors, focusing on algorithms, simulation techniques, and computational efficiency, are studied in detail. The design optimization by reducing the computational time and memory is presented. The algorithmic advances, benchmark simulation methods, analyzing surrogate model integration are addressed in this review paper. The optimization strategies leveraging the multi-static computation are assessed by selecting research papers, based on relevance, using the SciSpace platform assisted by artificial intelligence (AI). Studies include the finite element, surrogate modeling, model order reduction, and hybrid analytical approaches, along with their comparative and critical analyses. The parallel-in-space-and-time algorithms combined with model order reduction significantly enhance scalability and simulation speed without compromising accuracy are studied. Surrogate modeling techniques are studied in detail, such as the Gaussian processes and neural networks for reducing the computational complexity and enabling the rapid multi-objective optimization, though they require extensive training data and careful validation. Multi-static computation methods are compared with traditional finite element approaches in computational efficiency, particularly when integrated with advanced optimization algorithms, in generalizability and handling the nonlinear transient phenomena. The paper demonstrates the multi-static computation frameworks, augmented by surrogate models and optimization algorithms for a robust pathway for efficient and accurate IPM motor design.